Overview

Dataset statistics

Number of variables10
Number of observations74051
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

Length is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Diameter is highly overall correlated with Length and 6 other fieldsHigh correlation
Height is highly overall correlated with Length and 6 other fieldsHigh correlation
Weight is highly overall correlated with Length and 6 other fieldsHigh correlation
Shucked Weight is highly overall correlated with Length and 6 other fieldsHigh correlation
Viscera Weight is highly overall correlated with Length and 6 other fieldsHigh correlation
Shell Weight is highly overall correlated with Length and 6 other fieldsHigh correlation
Age is highly overall correlated with Length and 6 other fieldsHigh correlation
id is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2023-10-15 04:42:24.514645
Analysis finished2023-10-15 04:42:52.293374
Duration27.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct74051
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37025
Minimum0
Maximum74050
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size578.7 KiB
2023-10-15T09:42:52.625915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3702.5
Q118512.5
median37025
Q355537.5
95-th percentile70347.5
Maximum74050
Range74050
Interquartile range (IQR)37025

Descriptive statistics

Standard deviation21376.827
Coefficient of variation (CV)0.57736196
Kurtosis-1.2
Mean37025
Median Absolute Deviation (MAD)18513
Skewness0
Sum2.7417383 × 109
Variance4.5696872 × 108
MonotonicityStrictly increasing
2023-10-15T09:42:53.103009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
49320 1
 
< 0.1%
49372 1
 
< 0.1%
49371 1
 
< 0.1%
49370 1
 
< 0.1%
49369 1
 
< 0.1%
49368 1
 
< 0.1%
49367 1
 
< 0.1%
49366 1
 
< 0.1%
49365 1
 
< 0.1%
Other values (74041) 74041
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
74050 1
< 0.1%
74049 1
< 0.1%
74048 1
< 0.1%
74047 1
< 0.1%
74046 1
< 0.1%
74045 1
< 0.1%
74044 1
< 0.1%
74043 1
< 0.1%
74042 1
< 0.1%
74041 1
< 0.1%

Sex
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size578.7 KiB
M
27084 
I
23957 
F
23010 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74051
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd rowM
4th rowF
5th rowI

Common Values

ValueCountFrequency (%)
M 27084
36.6%
I 23957
32.4%
F 23010
31.1%

Length

2023-10-15T09:42:53.457859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-15T09:42:53.729359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 27084
36.6%
i 23957
32.4%
f 23010
31.1%

Most occurring characters

ValueCountFrequency (%)
M 27084
36.6%
I 23957
32.4%
F 23010
31.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 74051
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 27084
36.6%
I 23957
32.4%
F 23010
31.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 74051
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 27084
36.6%
I 23957
32.4%
F 23010
31.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 27084
36.6%
I 23957
32.4%
F 23010
31.1%

Length
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.31746
Minimum0.1875
Maximum2.0128145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.7 KiB
2023-10-15T09:42:54.125923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1875
5-th percentile0.7375
Q11.15
median1.375
Q31.5375
95-th percentile1.675
Maximum2.0128145
Range1.8253145
Interquartile range (IQR)0.3875

Descriptive statistics

Standard deviation0.28775713
Coefficient of variation (CV)0.21841811
Kurtosis0.29176672
Mean1.31746
Median Absolute Deviation (MAD)0.1875
Skewness-0.84437694
Sum97559.23
Variance0.082804163
MonotonicityNot monotonic
2023-10-15T09:42:54.419559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5625 2302
 
3.1%
1.5 2096
 
2.8%
1.375 1993
 
2.7%
1.3125 1869
 
2.5%
1.425 1820
 
2.5%
1.4375 1819
 
2.5%
1.45 1765
 
2.4%
1.6125 1686
 
2.3%
1.4125 1674
 
2.3%
1.575 1670
 
2.3%
Other values (134) 55357
74.8%
ValueCountFrequency (%)
0.1875 2
 
< 0.1%
0.275 2
 
< 0.1%
0.2875 1
 
< 0.1%
0.3 5
 
< 0.1%
0.3125 3
 
< 0.1%
0.325 12
< 0.1%
0.3375 9
< 0.1%
0.35 15
< 0.1%
0.3625 4
 
< 0.1%
0.375 21
< 0.1%
ValueCountFrequency (%)
2.0128145 1
 
< 0.1%
1.95 6
 
< 0.1%
1.9375 5
 
< 0.1%
1.925 19
 
< 0.1%
1.9125 5
 
< 0.1%
1.9 1
 
< 0.1%
1.8875 16
 
< 0.1%
1.875 64
0.1%
1.8625 29
< 0.1%
1.85 35
< 0.1%

Diameter
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.024496
Minimum0.1375
Maximum1.6125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.7 KiB
2023-10-15T09:42:54.743106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1375
5-th percentile0.55
Q10.8875
median1.075
Q31.2
95-th percentile1.3125
Maximum1.6125
Range1.475
Interquartile range (IQR)0.3125

Descriptive statistics

Standard deviation0.23739628
Coefficient of variation (CV)0.23172005
Kurtosis0.17727812
Mean1.024496
Median Absolute Deviation (MAD)0.15
Skewness-0.8128656
Sum75864.956
Variance0.056356993
MonotonicityNot monotonic
2023-10-15T09:42:55.272313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.125 3131
 
4.2%
1.1875 2467
 
3.3%
1.2125 2322
 
3.1%
1 2153
 
2.9%
1.175 2132
 
2.9%
1.2 2080
 
2.8%
1.25 2067
 
2.8%
1.1 1958
 
2.6%
1.3125 1909
 
2.6%
1.225 1821
 
2.5%
Other values (112) 52011
70.2%
ValueCountFrequency (%)
0.1375 5
 
< 0.1%
0.15 2
 
< 0.1%
0.1875 1
 
< 0.1%
0.2 1
 
< 0.1%
0.2125 2
 
< 0.1%
0.225 1
 
< 0.1%
0.2375 11
 
< 0.1%
0.25 20
 
< 0.1%
0.2625 49
0.1%
0.275 52
0.1%
ValueCountFrequency (%)
1.6125 1
 
< 0.1%
1.5875 1
 
< 0.1%
1.575 7
 
< 0.1%
1.5625 2
 
< 0.1%
1.55 2
 
< 0.1%
1.5375 6
 
< 0.1%
1.525 2
 
< 0.1%
1.5125 11
 
< 0.1%
1.5 30
< 0.1%
1.4875 16
< 0.1%

Height
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34808948
Minimum0
Maximum2.825
Zeros24
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size578.7 KiB
2023-10-15T09:42:55.651406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1875
Q10.3
median0.3625
Q30.4125
95-th percentile0.475
Maximum2.825
Range2.825
Interquartile range (IQR)0.1125

Descriptive statistics

Standard deviation0.092033961
Coefficient of variation (CV)0.26439742
Kurtosis14.153342
Mean0.34808948
Median Absolute Deviation (MAD)0.0625
Skewness0.086577684
Sum25776.374
Variance0.0084702499
MonotonicityNot monotonic
2023-10-15T09:42:55.944115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.375 5650
 
7.6%
0.4125 5446
 
7.4%
0.3125 4383
 
5.9%
0.4 4242
 
5.7%
0.3875 4242
 
5.7%
0.4375 4008
 
5.4%
0.3625 3451
 
4.7%
0.35 3291
 
4.4%
0.425 3016
 
4.1%
0.3375 3013
 
4.1%
Other values (55) 33309
45.0%
ValueCountFrequency (%)
0 24
 
< 0.1%
0.0125 1
 
< 0.1%
0.025 3
 
< 0.1%
0.0375 11
 
< 0.1%
0.05 6
 
< 0.1%
0.0625 95
 
0.1%
0.075 71
 
0.1%
0.0875 107
 
0.1%
0.1 298
0.4%
0.1125 305
0.4%
ValueCountFrequency (%)
2.825 2
 
< 0.1%
1.2875 1
 
< 0.1%
0.8125 1
 
< 0.1%
0.775 1
 
< 0.1%
0.7625 1
 
< 0.1%
0.6625 1
 
< 0.1%
0.625 13
< 0.1%
0.6125 1
 
< 0.1%
0.6 12
< 0.1%
0.5875 19
< 0.1%

Weight
Real number (ℝ)

HIGH CORRELATION 

Distinct3096
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.385217
Minimum0.056699
Maximum80.101512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.7 KiB
2023-10-15T09:42:56.409292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.056699
5-th percentile3.6003865
Q113.437663
median23.799405
Q332.162508
95-th percentile44.211045
Maximum80.101512
Range80.044813
Interquartile range (IQR)18.724845

Descriptive statistics

Standard deviation12.648153
Coefficient of variation (CV)0.54086105
Kurtosis-0.40179876
Mean23.385217
Median Absolute Deviation (MAD)9.4262087
Skewness0.23146456
Sum1731698.7
Variance159.97577
MonotonicityNot monotonic
2023-10-15T09:42:56.768538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.16250775 354
 
0.5%
6.30776375 294
 
0.4%
13.53688625 246
 
0.3%
27.499015 232
 
0.3%
34.5013415 222
 
0.3%
25.51455 203
 
0.3%
31.62386725 199
 
0.3%
33.40988575 194
 
0.3%
24.73493875 188
 
0.3%
18.01610725 188
 
0.3%
Other values (3086) 71731
96.9%
ValueCountFrequency (%)
0.056699 2
 
< 0.1%
0.12757275 1
 
< 0.1%
0.21262125 1
 
< 0.1%
0.226796 4
 
< 0.1%
0.29766975 12
 
< 0.1%
0.35436875 2
 
< 0.1%
0.3685435 6
 
< 0.1%
0.38271825 3
 
< 0.1%
0.396893 4
 
< 0.1%
0.41106775 37
< 0.1%
ValueCountFrequency (%)
80.10151225 3
 
< 0.1%
78.79743525 2
 
< 0.1%
75.3246215 3
 
< 0.1%
72.4329725 5
< 0.1%
72.291225 2
 
< 0.1%
72.234526 8
< 0.1%
71.71106475 1
 
< 0.1%
71.31316725 4
< 0.1%
71.11472075 2
 
< 0.1%
71.0154975 5
< 0.1%

Shucked Weight
Real number (ℝ)

HIGH CORRELATION 

Distinct1766
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.10427
Minimum0.0283495
Maximum42.184056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.7 KiB
2023-10-15T09:42:57.301676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0283495
5-th percentile1.5025235
Q15.7124242
median9.9081503
Q314.033003
95-th percentile19.376883
Maximum42.184056
Range42.155707
Interquartile range (IQR)8.3205783

Descriptive statistics

Standard deviation5.6180254
Coefficient of variation (CV)0.55600508
Kurtosis-0.11907427
Mean10.10427
Median Absolute Deviation (MAD)4.1673765
Skewness0.3494717
Sum748231.28
Variance31.562209
MonotonicityNot monotonic
2023-10-15T09:42:57.683056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.87980075 357
 
0.5%
2.721552 327
 
0.4%
14.92601175 322
 
0.4%
11.8784405 277
 
0.4%
7.10154975 271
 
0.4%
5.74077375 259
 
0.3%
10.149121 257
 
0.3%
19.6462035 230
 
0.3%
15.03940975 228
 
0.3%
4.9611625 227
 
0.3%
Other values (1756) 71296
96.3%
ValueCountFrequency (%)
0.0283495 2
 
< 0.1%
0.04252425 1
 
< 0.1%
0.07087375 3
 
< 0.1%
0.09922325 3
 
< 0.1%
0.12757275 7
 
< 0.1%
0.1417475 41
0.1%
0.15592225 23
 
< 0.1%
0.18427175 52
0.1%
0.1984465 1
 
< 0.1%
0.21262125 97
0.1%
ValueCountFrequency (%)
42.184056 2
 
< 0.1%
38.3001745 2
 
< 0.1%
38.2434755 1
 
< 0.1%
38.22930075 1
 
< 0.1%
36.55668025 1
 
< 0.1%
35.6920205 1
 
< 0.1%
35.5219235 1
 
< 0.1%
35.30930225 1
 
< 0.1%
35.13920525 5
< 0.1%
35.096681 1
 
< 0.1%

Viscera Weight
Real number (ℝ)

HIGH CORRELATION 

Distinct967
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.058386
Minimum0.04252425
Maximum21.54562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.7 KiB
2023-10-15T09:42:58.035439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04252425
5-th percentile0.793786
Q12.8632995
median4.989512
Q36.9881518
95-th percentile9.7380533
Maximum21.54562
Range21.503096
Interquartile range (IQR)4.1248522

Descriptive statistics

Standard deviation2.7927287
Coefficient of variation (CV)0.55209877
Kurtosis-0.365303
Mean5.058386
Median Absolute Deviation (MAD)2.0695135
Skewness0.28638284
Sum374578.54
Variance7.7993336
MonotonicityNot monotonic
2023-10-15T09:42:58.456220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.8683465 509
 
0.7%
6.22271525 432
 
0.6%
4.86193925 383
 
0.5%
3.86970675 372
 
0.5%
4.89028875 369
 
0.5%
1.7293195 346
 
0.5%
4.8477645 345
 
0.5%
6.2652395 345
 
0.5%
6.90310325 344
 
0.5%
3.6003865 344
 
0.5%
Other values (957) 70262
94.9%
ValueCountFrequency (%)
0.04252425 2
 
< 0.1%
0.056699 2
 
< 0.1%
0.07087375 6
 
< 0.1%
0.0750485 1
 
< 0.1%
0.0775255 1
 
< 0.1%
0.07756975 1
 
< 0.1%
0.0850485 38
0.1%
0.09922325 68
0.1%
0.113398 7
 
< 0.1%
0.12757275 73
0.1%
ValueCountFrequency (%)
21.54562 1
 
< 0.1%
19.74542675 1
 
< 0.1%
19.5895045 1
 
< 0.1%
18.18620425 1
 
< 0.1%
18.086981 1
 
< 0.1%
17.293195 1
 
< 0.1%
16.726205 2
< 0.1%
16.3009625 4
< 0.1%
15.989118 2
< 0.1%
15.6772735 1
 
< 0.1%

Shell Weight
Real number (ℝ)

HIGH CORRELATION 

Distinct1048
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7238701
Minimum0.04252425
Maximum28.491248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.7 KiB
2023-10-15T09:42:58.801330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04252425
5-th percentile1.1056305
Q13.96893
median6.9314528
Q39.07184
95-th percentile12.7431
Maximum28.491248
Range28.448723
Interquartile range (IQR)5.10291

Descriptive statistics

Standard deviation3.5843721
Coefficient of variation (CV)0.5330817
Kurtosis-0.1424151
Mean6.7238701
Median Absolute Deviation (MAD)2.5656297
Skewness0.27745897
Sum497909.31
Variance12.847723
MonotonicityNot monotonic
2023-10-15T09:42:59.206885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.7961125 1427
 
1.9%
8.9300925 1395
 
1.9%
7.2291225 1127
 
1.5%
5.2446575 1107
 
1.5%
10.0640725 1083
 
1.5%
1.984465 1054
 
1.4%
9.4970825 1049
 
1.4%
7.5126175 1009
 
1.4%
8.3631025 1000
 
1.4%
7.087375 993
 
1.3%
Other values (1038) 62807
84.8%
ValueCountFrequency (%)
0.04252425 2
 
< 0.1%
0.056699 1
 
< 0.1%
0.07087375 1
 
< 0.1%
0.09922325 6
 
< 0.1%
0.113398 9
 
< 0.1%
0.12757275 1
 
< 0.1%
0.1417475 197
0.3%
0.15592225 4
 
< 0.1%
0.170097 9
 
< 0.1%
0.18427175 17
 
< 0.1%
ValueCountFrequency (%)
28.4912475 1
 
< 0.1%
26.932025 1
 
< 0.1%
25.4295015 5
< 0.1%
25.0893075 5
< 0.1%
24.08290025 1
 
< 0.1%
23.43086175 1
 
< 0.1%
23.1048425 6
< 0.1%
22.60872625 1
 
< 0.1%
22.4244545 1
 
< 0.1%
22.35358075 1
 
< 0.1%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.967806
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size578.7 KiB
2023-10-15T09:42:59.551077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median10
Q311
95-th percentile16
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1751892
Coefficient of variation (CV)0.31854444
Kurtosis2.2966407
Mean9.967806
Median Absolute Deviation (MAD)2
Skewness1.0929192
Sum738126
Variance10.081826
MonotonicityNot monotonic
2023-10-15T09:42:59.854713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
9 12473
16.8%
10 11480
15.5%
8 9966
13.5%
11 8746
11.8%
7 6574
8.9%
12 4747
 
6.4%
6 4532
 
6.1%
13 3720
 
5.0%
14 2305
 
3.1%
5 1913
 
2.6%
Other values (18) 7595
10.3%
ValueCountFrequency (%)
1 16
 
< 0.1%
2 14
 
< 0.1%
3 213
 
0.3%
4 948
 
1.3%
5 1913
 
2.6%
6 4532
 
6.1%
7 6574
8.9%
8 9966
13.5%
9 12473
16.8%
10 11480
15.5%
ValueCountFrequency (%)
29 18
 
< 0.1%
27 33
 
< 0.1%
26 19
 
< 0.1%
25 21
 
< 0.1%
24 29
 
< 0.1%
23 120
 
0.2%
22 98
 
0.1%
21 241
0.3%
20 415
0.6%
19 588
0.8%

Interactions

2023-10-15T09:42:48.518009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:27.488673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:30.385665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:33.194139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:35.517029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:38.084651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:40.808827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:43.306359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:45.561783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:48.862424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:27.923354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:30.658940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:33.569908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:35.777331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:38.323358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:41.040531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:43.523771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:45.908895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:49.255371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:28.192590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:30.968108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:33.827809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:35.978933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:38.601883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:41.351296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:43.810036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:46.290148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:49.629229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:28.444944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:31.335207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:34.120855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:36.272192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:38.913060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:41.691880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:44.085564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:46.677803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:49.979432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:28.744501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:31.623549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:34.362166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:36.629414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:39.296421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:41.875086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:44.379900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:46.888481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:50.256723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:29.174714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:31.894351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:34.624492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:37.036482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:39.596149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:42.168533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:44.634409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:47.109778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:50.478657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:29.394537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:32.206633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:34.869927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:37.369085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:39.951226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:42.504524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:44.924633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:47.329694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:50.783807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:29.706911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:32.573800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:35.076843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:37.563148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:40.286339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:42.818665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:45.117364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:47.657817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:51.009237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:30.085851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:32.845072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:35.312610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:37.837415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:40.526683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:43.095952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:45.333785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-15T09:42:48.303393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-15T09:43:00.084782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idLengthDiameterHeightWeightShucked WeightViscera WeightShell WeightAgeSex
id1.000-0.001-0.0010.000-0.000-0.000-0.001-0.000-0.0000.000
Length-0.0011.0000.9840.9090.9770.9600.9580.9540.6690.471
Diameter-0.0010.9841.0000.9130.9770.9560.9570.9590.6790.476
Height0.0000.9090.9131.0000.9270.8910.9110.9310.7040.419
Weight-0.0000.9770.9770.9271.0000.9750.9750.9720.6900.487
Shucked Weight-0.0000.9600.9560.8910.9751.0000.9500.9260.6130.458
Viscera Weight-0.0010.9580.9570.9110.9750.9501.0000.9470.6770.480
Shell Weight-0.0000.9540.9590.9310.9720.9260.9471.0000.7360.479
Age-0.0000.6690.6790.7040.6900.6130.6770.7361.0000.424
Sex0.0000.4710.4760.4190.4870.4580.4800.4790.4241.000

Missing values

2023-10-15T09:42:51.286775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-15T09:42:51.854410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idSexLengthDiameterHeightWeightShucked WeightViscera WeightShell WeightAge
00I1.52501.17500.375028.97318912.7289266.6479588.3489289
11I1.10000.82500.275010.4184414.5217452.3246593.4019408
22M1.38751.11250.375024.77746311.3398005.5565026.6621339
33F1.70001.41250.500050.66055620.35494110.99183914.99688511
44I1.25001.01250.337523.28911411.9776644.5075705.9533958
55M1.50001.17500.412528.84561613.4093136.7897057.93786010
66M1.57501.13750.350030.02212011.9351407.3425218.64659811
77I1.31251.02500.350018.2996028.2497043.8980565.66990011
88F1.60001.28750.437538.82464016.9671767.41339410.77281012
99M1.02500.76250.262510.3050434.4933962.1262122.97669811
idSexLengthDiameterHeightWeightShucked WeightViscera WeightShell WeightAge
7404174041I1.32501.03750.325021.0069799.1001904.6067945.6699008
7404274042I1.13750.88750.312515.0394107.2432973.7279593.7846588
7404374043M1.25001.00000.300017.4632926.7755313.8980565.2446579
7404474044F1.57501.25000.387531.70891613.2959157.3283469.49708210
7404574045F1.62501.41250.487549.87453823.00561910.24834411.48154710
7404674046F1.66251.26250.437550.66055620.68096010.36174212.33203310
7404774047I1.07500.86250.275010.4467914.3232992.2963103.5436876
7404874048F1.48751.20000.412529.48348012.3036837.5409678.07960710
7404974049I1.21250.96250.312516.7687298.9726172.9199994.2807748
7405074050I0.91250.67500.20005.3864052.0553391.0347571.7009706